An important aspect to effective contact center operation lies in routing the right customer interactions to the right contact center agent. Such interactions may consist of telephone calls, emails, text messages, chat messages, and the like. Identifying the best agent helps to serve two purposes: (i) provide good experience for the caller; and (ii) reduce cost and/or improve revenue for the business. Customer contact centers (CC) traditionally employ skill-based routing for routing customer interactions. In traditional skill-based routing, the skill of an agent is one of the primary factors considered for determining whether the agent is equipped to deal with a particular interaction. The skill may relate to an agent's language proficiency, sales skill, certification, and the like. In this traditional approach to skill-based routing, explicit skill models are generated for the agents, and the skill models are used along with preset routing strategies for mapping the interactions to the agents.
One drawback to traditional skill-based routing using explicit skill models is that the models are often static and do not dynamically adapt based on real-time changes to the environment. Traditional skill-based matching also often results in a relatively large pool of agents that are deemed to have equivalent skills. Another drawback to traditional skill-based models is that they require manual effort to construct and maintain. Thus, the more refined the skill model, the more costly it is. Accordingly, what is desired is a system and method for matching customer interactions to agents to make those connections more optimal than matching based on traditional skill-based routing alone, where the matching may be done using models that may be constructed and/or maintained with minimized manual effort.